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Emerging trends in model risk management

Banks and financial providers lean heavily on models to manage risk, but what about the risks inherent in the models?

Benefits and challenges

Modeling has become a favored tool among banks, capital markets firms and financial services leaders, who can access a variety of advanced mathematical, statistical or numerical models to assess and manage risk. Liquidity, balance sheet and income statement scenarios all can be projected using modeling tools.

Models can help manage risk, but they come with risks of their own. Model error and model misuse can leave a bank exposed or at even greater risk.

We believe a possible solution is to establish a model risk management (MRM) framework with a focus on good governance including:

Model ownership: identifying and measuring model risk to which the institution is exposed

Model control: setting limits, following up and independently validating models

Model compliance: building processes to help perform ownership and control rules in accordance with accepted policies.

With a framework for testing models and a solid governance structure, financial providers will be better prepared to manage model risk.

"As the use of models increases, so does the possibility that any particular model may not properly capture financial or emerging risks."

Efficient modeling components

A comprehensive MRM approach that addresses ownership, control and compliance involves several structural and functional steps. We believe these components are essential to helping build a reliable MRM framework:

Analytics function: Following an ordered process that includes methodology, execution, documentation, processes and leveraging a cross-functional implementation and validation team, can help address all aspects of the model lifecycle.

Validation function: Independent monitoring and validation can address several broad areas of MRM.

Audit function: By performing self-testing, the audit function forms an additional MRM line of defense by challenging the model’s design, data reliability and risk management controls.

Combined, these functions provide a framework that supports identification and mitigation of risks associated with financial models.

Model management

With the right approach in place, the next steps are to evaluate and manage the models themselves. These three actions help cultivate strong models:

Model definition: Establishing what is (and isn’t) a model is a necessary first step. At their core, models have inputs, processing tools and reporting procedures. Beyond this exists some gray area that enterprises should assess thoroughly.

Risk rating and inventory: By rating risk, financial firms are prioritizing the level of review each model needs (more for high risk, less for low risk). An inventory can help firms track and prioritize their models.

Model measurement and aggregation: This new activity is still evolving, and can include model risk scorecards, assessing operational risk and evaluating model uncertainty.

Taking these steps can help banks and capital markets firms master the models in their service. Risks inherent in models will not disappear, but they will be known and tightly managed, thus supporting strong mitigation.

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